subtee: Subgroup Treatment Effect Estimation in Clinical Trials

Naive and adjusted treatment effect estimation for subgroups. Model
averaging (Bornkamp et.al, 2016 <doi:10.1002/pst.1796>) and bagging (Rosenkranz, 2016 <doi:10.1002/bimj.201500147>) are proposed to address the problem of selection bias in
treatment effect estimates for subgroups. The package can be used for all
commonly encountered type of outcomes in clinical trials (continuous, binary,
survival, count). Additional functions are provided to build the subgroup
variables to be used and to plot the results using forest plots.